I am creating and using OLS Regression models using historical data to forecast quarterly balances for banking products (loans, mortgages, deposits, etc) for the Dodd-Frank/CCAR exercises. One problem we have run into is that sometimes the last historical time period's value (jump-off point which is used to start forecasting from) can be unexpectedly high or low (possibly due to a business action like a temporary interest rate change for marketing purposes or maybe an unusual event in the marketplace). This creates a problem in that the forecast generated is unusually high or low due to the out of the ordinary jump-off point. Any ideas on how to adjust for this in the model? An initial thought is to forecast from a previous data point where the growth rate of the data point is within a certain acceptable range. Any thoughts, ideas, or references to scholarly papers on this topic would be helpful. Thanks.
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